Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
MFBO-SSM: Multi-Fidelity Bayesian Optimization for Fast Inference in State-Space Models
Authors: Mahdi Imani, Seyede Fatemeh Ghoreishi, Douglas Allaire, Ulisses M. Braga-Neto7858-7865
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The accuracy and speed of the algorithm are demonstrated by numerical experiments using synthetic gene expression data from a gene regulatory network model and real data from the VIX stock price index. |
| Researcher Affiliation | Academia | Mahdi Imani Texas A&M University EMAIL Seyede Fatemeh Ghoreishi Texas A&M University EMAIL Douglas Allaire Texas A&M University EMAIL Ulisses M. Braga-Neto Texas A&M University EMAIL |
| Pseudocode | Yes | Algorithm 1 MFBO-SSM Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the methodology. |
| Open Datasets | No | The paper mentions using |
| Dataset Splits | No | The paper describes data generation and lengths (e.g., |
| Hardware Specification | Yes | All experiments have been conducted on a PC with an Intel Core i7-4790 CPU@3.60-GHz clock and 16 GB of RAM. |
| Software Dependencies | No | The paper does not specify software names with version numbers for its dependencies. |
| Experiment Setup | Yes | MFBO-SSM algorithm uses N1 = 100, N2 = 1000, N3 = 5000, corresponding to small, medium, and large particle sample sizes. Other methods use a fixed particle sample size N = 1000. [...] We are interested in estimating the true parameter θ = (σ , φ , β , µ ) = (0.97, 0.55, 0.95, 0.1) from synthetic data, where Θ = [0, 2] [ 1, 1] [0, 10] [0, 5]. [...] The MFBO-SSM, BO, EM, and ML algorithms all stop when the change in the estimated value of all parameters over a window of length 20 falls bellow 5% of their range, whereas the PMMH algorithm continues over a fixed number of 6,000 iterations. |